{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FhGuhbZ6M5tl"
   },
   "source": [
    "##### Copyright 2018 The TensorFlow Authors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cellView": "form",
    "execution": {
     "iopub.execute_input": "2020-09-22T22:14:11.659661Z",
     "iopub.status.busy": "2020-09-22T22:14:11.659015Z",
     "iopub.status.idle": "2020-09-22T22:14:11.661400Z",
     "shell.execute_reply": "2020-09-22T22:14:11.660846Z"
    },
    "id": "AwOEIRJC6Une"
   },
   "outputs": [],
   "source": [
    "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "cellView": "form",
    "execution": {
     "iopub.execute_input": "2020-09-22T22:14:11.665441Z",
     "iopub.status.busy": "2020-09-22T22:14:11.664730Z",
     "iopub.status.idle": "2020-09-22T22:14:11.666623Z",
     "shell.execute_reply": "2020-09-22T22:14:11.667045Z"
    },
    "id": "KyPEtTqk6VdG"
   },
   "outputs": [],
   "source": [
    "#@title MIT License\n",
    "#\n",
    "# Copyright (c) 2017 François Chollet\n",
    "#\n",
    "# Permission is hereby granted, free of charge, to any person obtaining a\n",
    "# copy of this software and associated documentation files (the \"Software\"),\n",
    "# to deal in the Software without restriction, including without limitation\n",
    "# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n",
    "# and/or sell copies of the Software, and to permit persons to whom the\n",
    "# Software is furnished to do so, subject to the following conditions:\n",
    "#\n",
    "# The above copyright notice and this permission notice shall be included in\n",
    "# all copies or substantial portions of the Software.\n",
    "#\n",
    "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
    "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
    "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n",
    "# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
    "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n",
    "# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n",
    "# DEALINGS IN THE SOFTWARE."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EIdT9iu_Z4Rb"
   },
   "source": [
    "# Basic regression: Predict fuel efficiency"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AHp3M9ZmrIxj"
   },
   "source": [
    "Note: 我们的 TensorFlow 社区翻译了这些文档。因为社区翻译是尽力而为， 所以无法保证它们是最准确的，并且反映了最新的\n",
    "[官方英文文档](https://tensorflow.google.cn/?hl=en)。如果您有改进此翻译的建议， 请提交 pull request 到\n",
    "[tensorflow/docs](https://github.com/tensorflow/docs) GitHub 仓库。要志愿地撰写或者审核译文，请加入\n",
    "[docs-zh-cn@tensorflow.org Google Group](https://groups.google.com/a/tensorflow.org/forum/#!forum/docs-zh-cn)。\n",
    "\n",
    "在 *回归 (regression)* 问题中，我们的目的是预测出如价格或概率这样连续值的输出。相对于*分类(classification)* 问题，*分类(classification)* 的目的是从一系列的分类出选择出一个分类 （如，给出一张包含苹果或橘子的图片，识别出图片中是哪种水果）。\n",
    "\n",
    "本 notebook 使用经典的 [Auto MPG](https://archive.ics.uci.edu/ml/datasets/auto+mpg) 数据集，构建了一个用来预测70年代末到80年代初汽车燃油效率的模型。为了做到这一点，我们将为该模型提供许多那个时期的汽车描述。这个描述包含：气缸数，排量，马力以及重量。\n",
    "\n",
    "本示例使用 `tf.keras` API，相关细节请参阅 [本指南](https://tensorflow.google.cn/guide/keras)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-22T22:14:11.674853Z",
     "iopub.status.busy": "2020-09-22T22:14:11.674159Z",
     "iopub.status.idle": "2020-09-22T22:14:13.030140Z",
     "shell.execute_reply": "2020-09-22T22:14:13.029443Z"
    },
    "id": "moB4tpEHxKB3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: You are using pip version 20.2.2; however, version 20.2.3 is available.\r\n",
      "You should consider upgrading via the '/tmpfs/src/tf_docs_env/bin/python -m pip install --upgrade pip' command.\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "# 使用 seaborn 绘制矩阵图 (pairplot)\n",
    "#!pip install -q seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-22T22:14:13.035964Z",
     "iopub.status.busy": "2020-09-22T22:14:13.035128Z",
     "iopub.status.idle": "2020-09-22T22:14:19.777490Z",
     "shell.execute_reply": "2020-09-22T22:14:19.776963Z"
    },
    "id": "1rRo8oNqZ-Rj"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.10.0\n"
     ]
    }
   ],
   "source": [
    "import pathlib\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F_72b0LCNbjx"
   },
   "source": [
    "## Auto MPG 数据集\n",
    "\n",
    "该数据集可以从 [UCI机器学习库](https://archive.ics.uci.edu/ml/) 中获取.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gFh9ne3FZ-On"
   },
   "source": [
    "### 获取数据\n",
    "首先下载数据集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-22T22:14:19.783750Z",
     "iopub.status.busy": "2020-09-22T22:14:19.783068Z",
     "iopub.status.idle": "2020-09-22T22:14:20.118365Z",
     "shell.execute_reply": "2020-09-22T22:14:20.117816Z"
    },
    "id": "p9kxxgzvzlyz"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "'<' not supported between instances of 'int' and 'NoneType'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[3], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m dataset_path \u001b[38;5;241m=\u001b[39m \u001b[43mkeras\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mutils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_file\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mauto-mpg.data\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mhttp://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      2\u001b[0m dataset_path\n",
      "File \u001b[1;32mc:\\Users\\Dev\\.conda\\envs\\dl4tensorflow\\lib\\site-packages\\keras\\utils\\data_utils.py:296\u001b[0m, in \u001b[0;36mget_file\u001b[1;34m(fname, origin, untar, md5_hash, file_hash, cache_subdir, hash_algorithm, extract, archive_format, cache_dir)\u001b[0m\n\u001b[0;32m    294\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    295\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 296\u001b[0m         \u001b[43murlretrieve\u001b[49m\u001b[43m(\u001b[49m\u001b[43morigin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mDLProgbar\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    297\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m urllib\u001b[38;5;241m.\u001b[39merror\u001b[38;5;241m.\u001b[39mHTTPError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    298\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(error_msg\u001b[38;5;241m.\u001b[39mformat(origin, e\u001b[38;5;241m.\u001b[39mcode, e\u001b[38;5;241m.\u001b[39mmsg))\n",
      "File \u001b[1;32mc:\\Users\\Dev\\.conda\\envs\\dl4tensorflow\\lib\\site-packages\\keras\\utils\\data_utils.py:86\u001b[0m, in \u001b[0;36murlretrieve\u001b[1;34m(url, filename, reporthook, data)\u001b[0m\n\u001b[0;32m     84\u001b[0m response \u001b[38;5;241m=\u001b[39m urlopen(url, data)\n\u001b[0;32m     85\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(filename, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwb\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m fd:\n\u001b[1;32m---> 86\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m chunk_read(response, reporthook\u001b[38;5;241m=\u001b[39mreporthook):\n\u001b[0;32m     87\u001b[0m         fd\u001b[38;5;241m.\u001b[39mwrite(chunk)\n",
      "File \u001b[1;32mc:\\Users\\Dev\\.conda\\envs\\dl4tensorflow\\lib\\site-packages\\keras\\utils\\data_utils.py:78\u001b[0m, in \u001b[0;36murlretrieve.<locals>.chunk_read\u001b[1;34m(response, chunk_size, reporthook)\u001b[0m\n\u001b[0;32m     76\u001b[0m count \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m     77\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m reporthook \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m---> 78\u001b[0m     \u001b[43mreporthook\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcount\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchunk_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtotal_size\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     79\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunk:\n\u001b[0;32m     80\u001b[0m     \u001b[38;5;28;01myield\u001b[39;00m chunk\n",
      "File \u001b[1;32mc:\\Users\\Dev\\.conda\\envs\\dl4tensorflow\\lib\\site-packages\\keras\\utils\\data_utils.py:287\u001b[0m, in \u001b[0;36mget_file.<locals>.DLProgbar.__call__\u001b[1;34m(self, block_num, block_size, total_size)\u001b[0m\n\u001b[0;32m    285\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprogbar \u001b[38;5;241m=\u001b[39m Progbar(total_size)\n\u001b[0;32m    286\u001b[0m current \u001b[38;5;241m=\u001b[39m block_num \u001b[38;5;241m*\u001b[39m block_size\n\u001b[1;32m--> 287\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mcurrent\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m<\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mtotal_size\u001b[49m:\n\u001b[0;32m    288\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprogbar\u001b[38;5;241m.\u001b[39mupdate(current)\n\u001b[0;32m    289\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfinished:\n",
      "\u001b[1;31mTypeError\u001b[0m: '<' not supported between instances of 'int' and 'NoneType'"
     ]
    }
   ],
   "source": [
    "dataset_path = keras.utils.get_file(\"auto-mpg.data\", \"http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data\")\n",
    "dataset_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nslsRLh7Zss4"
   },
   "source": [
    "使用 pandas 导入数据集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-22T22:14:20.127393Z",
     "iopub.status.busy": "2020-09-22T22:14:20.126696Z",
     "iopub.status.idle": "2020-09-22T22:14:20.146592Z",
     "shell.execute_reply": "2020-09-22T22:14:20.146032Z"
    },
    "id": "CiX2FI4gZtTt"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'dataset_path' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[4], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m column_names \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMPG\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCylinders\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDisplacement\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mHorsepower\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mWeight\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m      2\u001b[0m                 \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAcceleration\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mModel Year\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOrigin\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m----> 3\u001b[0m raw_dataset \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[43mdataset_path\u001b[49m, names\u001b[38;5;241m=\u001b[39mcolumn_names,\n\u001b[0;32m      4\u001b[0m                       na_values \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m?\u001b[39m\u001b[38;5;124m\"\u001b[39m, comment\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m      5\u001b[0m                       sep\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m, skipinitialspace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m      7\u001b[0m dataset \u001b[38;5;241m=\u001b[39m raw_dataset\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m      8\u001b[0m dataset\u001b[38;5;241m.\u001b[39mtail()\n",
      "\u001b[1;31mNameError\u001b[0m: name 'dataset_path' is not defined"
     ]
    }
   ],
   "source": [
    "column_names = ['MPG','Cylinders','Displacement','Horsepower','Weight',\n",
    "                'Acceleration', 'Model Year', 'Origin']\n",
    "raw_dataset = pd.read_csv(dataset_path, names=column_names,\n",
    "                      na_values = \"?\", comment='\\t',\n",
    "                      sep=\" \", skipinitialspace=True)\n",
    "\n",
    "dataset = raw_dataset.copy()\n",
    "dataset.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3MWuJTKEDM-f"
   },
   "source": [
    "### 数据清洗\n",
    "\n",
    "数据集中包括一些未知值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-22T22:14:20.153046Z",
     "iopub.status.busy": "2020-09-22T22:14:20.152371Z",
     "iopub.status.idle": "2020-09-22T22:14:20.155873Z",
     "shell.execute_reply": "2020-09-22T22:14:20.155382Z"
    },
    "id": "JEJHhN65a2VV"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MPG             0\n",
       "Cylinders       0\n",
       "Displacement    0\n",
       "Horsepower      6\n",
       "Weight          0\n",
       "Acceleration    0\n",
       "Model Year      0\n",
       "Origin          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9UPN0KBHa_WI"
   },
   "source": [
    "为了保证这个初始示例的简单性，删除这些行。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-22T22:14:20.161336Z",
     "iopub.status.busy": "2020-09-22T22:14:20.160685Z",
     "iopub.status.idle": "2020-09-22T22:14:20.165291Z",
     "shell.execute_reply": "2020-09-22T22:14:20.164784Z"
    },
    "id": "4ZUDosChC1UN"
   },
   "outputs": [],
   "source": [
    "dataset = dataset.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8XKitwaH4v8h"
   },
   "source": [
    "`\"Origin\"` 列实际上代表分类，而不仅仅是一个数字。所以把它转换为独热码 （one-hot）:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-22T22:14:20.170275Z",
     "iopub.status.busy": "2020-09-22T22:14:20.169619Z",
     "iopub.status.idle": "2020-09-22T22:14:20.172206Z",
     "shell.execute_reply": "2020-09-22T22:14:20.171664Z"
    },
    "id": "gWNTD2QjBWFJ"
   },
   "outputs": [],
   "source": [
    "origin = dataset.pop('Origin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-22T22:14:20.188389Z",
     "iopub.status.busy": "2020-09-22T22:14:20.187705Z",
     "iopub.status.idle": "2020-09-22T22:14:20.191424Z",
     "shell.execute_reply": "2020-09-22T22:14:20.190921Z"
    },
    "id": "ulXz4J7PAUzk"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MPG</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>Model Year</th>\n",
       "      <th>USA</th>\n",
       "      <th>Europe</th>\n",
       "      <th>Japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2790.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2130.0</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2295.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2625.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n",
       "393  27.0          4         140.0        86.0  2790.0          15.6   \n",
       "394  44.0          4          97.0        52.0  2130.0          24.6   \n",
       "395  32.0          4         135.0        84.0  2295.0          11.6   \n",
       "396  28.0          4         120.0        79.0  2625.0          18.6   \n",
       "397  31.0          4         119.0        82.0  2720.0          19.4   \n",
       "\n",
       "     Model Year  USA  Europe  Japan  \n",
       "393          82  1.0     0.0    0.0  \n",
       "394          82  0.0     1.0    0.0  \n",
       "395          82  1.0     0.0    0.0  \n",
       "396          82  1.0     0.0    0.0  \n",
       "397          82  1.0     0.0    0.0  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['USA'] = (origin == 1)*1.0\n",
    "dataset['Europe'] = (origin == 2)*1.0\n",
    "dataset['Japan'] = (origin == 3)*1.0\n",
    "dataset.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Cuym4yvk76vU"
   },
   "source": [
    "### 拆分训练数据集和测试数据集\n",
    "\n",
    "现在需要将数据集拆分为一个训练数据集和一个测试数据集。\n",
    "\n",
    "我们最后将使用测试数据集对模型进行评估。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-22T22:14:20.197718Z",
     "iopub.status.busy": "2020-09-22T22:14:20.197100Z",
     "iopub.status.idle": "2020-09-22T22:14:20.199264Z",
     "shell.execute_reply": "2020-09-22T22:14:20.199671Z"
    },
    "id": "qn-IGhUE7_1H"
   },
   "outputs": [],
   "source": [
    "train_dataset = dataset.sample(frac=0.8,random_state=0)\n",
    "test_dataset = dataset.drop(train_dataset.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J4ubs136WLNp"
   },
   "source": [
    "### 数据检查\n",
    "\n",
    "快速查看训练集中几对列的联合分布。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-22T22:14:20.219713Z",
     "iopub.status.busy": "2020-09-22T22:14:20.217812Z",
     "iopub.status.idle": "2020-09-22T22:14:25.479837Z",
     "shell.execute_reply": "2020-09-22T22:14:25.480479Z"
    },
    "id": "oRKO_x8gWKv-"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f708ca93e80>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(train_dataset[[\"MPG\", \"Cylinders\", \"Displacement\", \"Weight\"]], diag_kind=\"kde\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gavKO_6DWRMP"
   },
   "source": [
    "也可以查看总体的数据统计:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-22T22:14:25.491308Z",
     "iopub.status.busy": "2020-09-22T22:14:25.488672Z",
     "iopub.status.idle": "2020-09-22T22:14:25.524462Z",
     "shell.execute_reply": "2020-09-22T22:14:25.524920Z"
    },
    "id": "yi2FzC3T21jR"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cylinders</th>\n",
       "      <td>314.0</td>\n",
       "      <td>5.477707</td>\n",
       "      <td>1.699788</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Displacement</th>\n",
       "      <td>314.0</td>\n",
       "      <td>195.318471</td>\n",
       "      <td>104.331589</td>\n",
       "      <td>68.0</td>\n",
       "      <td>105.50</td>\n",
       "      <td>151.0</td>\n",
       "      <td>265.75</td>\n",
       "      <td>455.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horsepower</th>\n",
       "      <td>314.0</td>\n",
       "      <td>104.869427</td>\n",
       "      <td>38.096214</td>\n",
       "      <td>46.0</td>\n",
       "      <td>76.25</td>\n",
       "      <td>94.5</td>\n",
       "      <td>128.00</td>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight</th>\n",
       "      <td>314.0</td>\n",
       "      <td>2990.251592</td>\n",
       "      <td>843.898596</td>\n",
       "      <td>1649.0</td>\n",
       "      <td>2256.50</td>\n",
       "      <td>2822.5</td>\n",
       "      <td>3608.00</td>\n",
       "      <td>5140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acceleration</th>\n",
       "      <td>314.0</td>\n",
       "      <td>15.559236</td>\n",
       "      <td>2.789230</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.80</td>\n",
       "      <td>15.5</td>\n",
       "      <td>17.20</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model Year</th>\n",
       "      <td>314.0</td>\n",
       "      <td>75.898089</td>\n",
       "      <td>3.675642</td>\n",
       "      <td>70.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>76.0</td>\n",
       "      <td>79.00</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>314.0</td>\n",
       "      <td>0.624204</td>\n",
       "      <td>0.485101</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>314.0</td>\n",
       "      <td>0.178344</td>\n",
       "      <td>0.383413</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>314.0</td>\n",
       "      <td>0.197452</td>\n",
       "      <td>0.398712</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count         mean         std     min      25%     50%  \\\n",
       "Cylinders     314.0     5.477707    1.699788     3.0     4.00     4.0   \n",
       "Displacement  314.0   195.318471  104.331589    68.0   105.50   151.0   \n",
       "Horsepower    314.0   104.869427   38.096214    46.0    76.25    94.5   \n",
       "Weight        314.0  2990.251592  843.898596  1649.0  2256.50  2822.5   \n",
       "Acceleration  314.0    15.559236    2.789230     8.0    13.80    15.5   \n",
       "Model Year    314.0    75.898089    3.675642    70.0    73.00    76.0   \n",
       "USA           314.0     0.624204    0.485101     0.0     0.00     1.0   \n",
       "Europe        314.0     0.178344    0.383413     0.0     0.00     0.0   \n",
       "Japan         314.0     0.197452    0.398712     0.0     0.00     0.0   \n",
       "\n",
       "                  75%     max  \n",
       "Cylinders        8.00     8.0  \n",
       "Displacement   265.75   455.0  \n",
       "Horsepower     128.00   225.0  \n",
       "Weight        3608.00  5140.0  \n",
       "Acceleration    17.20    24.8  \n",
       "Model Year      79.00    82.0  \n",
       "USA              1.00     1.0  \n",
       "Europe           0.00     1.0  \n",
       "Japan            0.00     1.0  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_stats = train_dataset.describe()\n",
    "train_stats.pop(\"MPG\")\n",
    "train_stats = train_stats.transpose()\n",
    "train_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Db7Auq1yXUvh"
   },
   "source": [
    "### 从标签中分离特征\n",
    "\n",
    "将特征值从目标值或者\"标签\"中分离。 这个标签是你使用训练模型进行预测的值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-22T22:14:25.530486Z",
     "iopub.status.busy": "2020-09-22T22:14:25.529788Z",
     "iopub.status.idle": "2020-09-22T22:14:25.531970Z",
     "shell.execute_reply": "2020-09-22T22:14:25.532384Z"
    },
    "id": "t2sluJdCW7jN"
   },
   "outputs": [],
   "source": [
    "train_labels = train_dataset.pop('MPG')\n",
    "test_labels = test_dataset.pop('MPG')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mRklxK5s388r"
   },
   "source": [
    "### 数据规范化\n",
    "\n",
    "再次审视下上面的 `train_stats` 部分，并注意每个特征的范围有什么不同。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-ywmerQ6dSox"
   },
   "source": [
    "使用不同的尺度和范围对特征归一化是好的实践。尽管模型*可能* 在没有特征归一化的情况下收敛，它会使得模型训练更加复杂，并会造成生成的模型依赖输入所使用的单位选择。\n",
    "\n",
    "注意：尽管我们仅仅从训练集中有意生成这些统计数据，但是这些统计信息也会用于归一化的测试数据集。我们需要这样做，将测试数据集放入到与已经训练过的模型相同的分布中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-22T22:14:25.541061Z",
     "iopub.status.busy": "2020-09-22T22:14:25.540428Z",
     "iopub.status.idle": "2020-09-22T22:14:25.543101Z",
     "shell.execute_reply": "2020-09-22T22:14:25.542575Z"
    },
    "id": "JlC5ooJrgjQF"
   },
   "outputs": [],
   "source": [
    "def norm(x):\n",
    "  return (x - train_stats['mean']) / train_stats['std']\n",
    "normed_train_data = norm(train_dataset)\n",
    "normed_test_data = norm(test_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BuiClDk45eS4"
   },
   "source": [
    "我们将会使用这个已经归一化的数据来训练模型。\n",
    "\n",
    "警告: 用于归一化输入的数据统计（均值和标准差）需要反馈给模型从而应用于任何其他数据，以及我们之前所获得独热码。这些数据包含测试数据集以及生产环境中所使用的实时数据。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SmjdzxKzEu1-"
   },
   "source": [
    "## 模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6SWtkIjhrZwa"
   },
   "source": [
    "### 构建模型\n",
    "\n",
    "让我们来构建我们自己的模型。这里，我们将会使用一个“顺序”模型，其中包含两个紧密相连的隐藏层，以及返回单个、连续值得输出层。模型的构建步骤包含于一个名叫 'build_model' 的函数中，稍后我们将会创建第二个模型。 两个密集连接的隐藏层。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-22T22:14:25.548977Z",
     "iopub.status.busy": "2020-09-22T22:14:25.548321Z",
     "iopub.status.idle": "2020-09-22T22:14:25.551081Z",
     "shell.execute_reply": "2020-09-22T22:14:25.550509Z"
    },
    "id": "c26juK7ZG8j-"
   },
   "outputs": [],
   "source": [
    "def build_model():\n",
    "  model = keras.Sequential([\n",
    "    layers.Dense(64, activation='relu', input_shape=[len(train_dataset.keys())]),\n",
    "    layers.Dense(64, activation='relu'),\n",
    "    layers.Dense(1)\n",
    "  ])\n",
    "\n",
    "  optimizer = tf.keras.optimizers.RMSprop(0.001)\n",
    "\n",
    "  model.compile(loss='mse',\n",
    "                optimizer=optimizer,\n",
    "                metrics=['mae', 'mse'])\n",
    "  return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-22T22:14:25.555057Z",
     "iopub.status.busy": "2020-09-22T22:14:25.554417Z",
     "iopub.status.idle": "2020-09-22T22:14:27.341172Z",
     "shell.execute_reply": "2020-09-22T22:14:27.340559Z"
    },
    "id": "cGbPb-PHGbhs"
   },
   "outputs": [],
   "source": [
    "model = build_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Sj49Og4YGULr"
   },
   "source": [
    "### 检查模型\n",
    "\n",
    "使用 `.summary` 方法来打印该模型的简单描述。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-22T22:14:27.347191Z",
     "iopub.status.busy": "2020-09-22T22:14:27.346535Z",
     "iopub.status.idle": "2020-09-22T22:14:27.350874Z",
     "shell.execute_reply": "2020-09-22T22:14:27.350336Z"
    },
    "id": "ReAD0n6MsFK-"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 64)                640       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 4,865\n",
      "Trainable params: 4,865\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Vt6W50qGsJAL"
   },
   "source": [
    "现在试用下这个模型。从训练数据中批量获取‘10’条例子并对这些例子调用 `model.predict` 。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-22T22:14:27.356790Z",
     "iopub.status.busy": "2020-09-22T22:14:27.356094Z",
     "iopub.status.idle": "2020-09-22T22:14:27.754495Z",
     "shell.execute_reply": "2020-09-22T22:14:27.753871Z"
    },
    "id": "-d-gBaVtGTSC"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.15074062],\n",
       "       [0.0973136 ],\n",
       "       [0.17310914],\n",
       "       [0.08873479],\n",
       "       [0.52456   ],\n",
       "       [0.05311462],\n",
       "       [0.49406645],\n",
       "       [0.04333409],\n",
       "       [0.12005241],\n",
       "       [0.6703117 ]], dtype=float32)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_batch = normed_train_data[:10]\n",
    "example_result = model.predict(example_batch)\n",
    "example_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QlM8KrSOsaYo"
   },
   "source": [
    "它似乎在工作，并产生了预期的形状和类型的结果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0-qWCsh6DlyH"
   },
   "source": [
    "### 训练模型\n",
    "\n",
    "对模型进行1000个周期的训练，并在 `history` 对象中记录训练和验证的准确性。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-22T22:14:27.762751Z",
     "iopub.status.busy": "2020-09-22T22:14:27.762001Z",
     "iopub.status.idle": "2020-09-22T22:15:02.009664Z",
     "shell.execute_reply": "2020-09-22T22:15:02.009072Z"
    },
    "id": "sD7qHCmNIOY0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "......."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
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      "......"
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    },
    {
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      "......"
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    },
    {
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      "......"
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    },
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      "......."
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      "......."
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      "......"
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      "......"
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      "......."
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      "......."
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      "......"
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    },
    {
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     "output_type": "stream",
     "text": [
      "...\n",
      "...."
     ]
    },
    {
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      "......."
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      "......."
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      "......."
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      "......"
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      "......"
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      "......"
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      "......."
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      "......"
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      "......"
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      "......"
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      "......."
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      "......"
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      "......"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "......"
     ]
    },
    {
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     "output_type": "stream",
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      "......"
     ]
    },
    {
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      "......"
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      "......"
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      "......"
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      "......"
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      "......"
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      "......"
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      "......"
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      "......"
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    },
    {
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      "......"
     ]
    },
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      "......."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "......"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "...\n",
      "...."
     ]
    },
    {
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      "......."
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    },
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      "......."
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      "......"
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    },
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      "......."
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      "......."
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    },
    {
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     "output_type": "stream",
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      "......"
     ]
    },
    {
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      "......."
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    {
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     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "......."
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     "output_type": "stream",
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      "......."
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    {
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     "output_type": "stream",
     "text": [
      "."
     ]
    }
   ],
   "source": [
    "# 通过为每个完成的时期打印一个点来显示训练进度\n",
    "class PrintDot(keras.callbacks.Callback):\n",
    "  def on_epoch_end(self, epoch, logs):\n",
    "    if epoch % 100 == 0: print('')\n",
    "    print('.', end='')\n",
    "\n",
    "EPOCHS = 1000\n",
    "\n",
    "history = model.fit(\n",
    "  normed_train_data, train_labels,\n",
    "  epochs=EPOCHS, validation_split = 0.2, verbose=0,\n",
    "  callbacks=[PrintDot()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tQm3pc0FYPQB"
   },
   "source": [
    "使用 `history` 对象中存储的统计信息可视化模型的训练进度。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-22T22:15:02.024841Z",
     "iopub.status.busy": "2020-09-22T22:15:02.024078Z",
     "iopub.status.idle": "2020-09-22T22:15:02.027499Z",
     "shell.execute_reply": "2020-09-22T22:15:02.026984Z"
    },
    "id": "4Xj91b-dymEy"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loss</th>\n",
       "      <th>mae</th>\n",
       "      <th>mse</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_mae</th>\n",
       "      <th>val_mse</th>\n",
       "      <th>epoch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>2.570732</td>\n",
       "      <td>1.051618</td>\n",
       "      <td>2.570732</td>\n",
       "      <td>10.587498</td>\n",
       "      <td>2.456362</td>\n",
       "      <td>10.587498</td>\n",
       "      <td>995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2.660562</td>\n",
       "      <td>1.022598</td>\n",
       "      <td>2.660562</td>\n",
       "      <td>10.711611</td>\n",
       "      <td>2.428715</td>\n",
       "      <td>10.711611</td>\n",
       "      <td>996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>3.080793</td>\n",
       "      <td>1.141696</td>\n",
       "      <td>3.080793</td>\n",
       "      <td>10.469919</td>\n",
       "      <td>2.439384</td>\n",
       "      <td>10.469919</td>\n",
       "      <td>997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>2.729193</td>\n",
       "      <td>1.066712</td>\n",
       "      <td>2.729193</td>\n",
       "      <td>10.671435</td>\n",
       "      <td>2.500411</td>\n",
       "      <td>10.671435</td>\n",
       "      <td>998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>2.847594</td>\n",
       "      <td>1.041974</td>\n",
       "      <td>2.847594</td>\n",
       "      <td>10.892761</td>\n",
       "      <td>2.450137</td>\n",
       "      <td>10.892761</td>\n",
       "      <td>999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         loss       mae       mse   val_loss   val_mae    val_mse  epoch\n",
       "995  2.570732  1.051618  2.570732  10.587498  2.456362  10.587498    995\n",
       "996  2.660562  1.022598  2.660562  10.711611  2.428715  10.711611    996\n",
       "997  3.080793  1.141696  3.080793  10.469919  2.439384  10.469919    997\n",
       "998  2.729193  1.066712  2.729193  10.671435  2.500411  10.671435    998\n",
       "999  2.847594  1.041974  2.847594  10.892761  2.450137  10.892761    999"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist = pd.DataFrame(history.history)\n",
    "hist['epoch'] = history.epoch\n",
    "hist.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-22T22:15:02.036404Z",
     "iopub.status.busy": "2020-09-22T22:15:02.035618Z",
     "iopub.status.idle": "2020-09-22T22:15:02.597567Z",
     "shell.execute_reply": "2020-09-22T22:15:02.597008Z"
    },
    "id": "B6XriGbVPh2t"
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_history(history):\n",
    "  hist = pd.DataFrame(history.history)\n",
    "  hist['epoch'] = history.epoch\n",
    "\n",
    "  plt.figure()\n",
    "  plt.xlabel('Epoch')\n",
    "  plt.ylabel('Mean Abs Error [MPG]')\n",
    "  plt.plot(hist['epoch'], hist['mae'],\n",
    "           label='Train Error')\n",
    "  plt.plot(hist['epoch'], hist['val_mae'],\n",
    "           label = 'Val Error')\n",
    "  plt.ylim([0,5])\n",
    "  plt.legend()\n",
    "\n",
    "  plt.figure()\n",
    "  plt.xlabel('Epoch')\n",
    "  plt.ylabel('Mean Square Error [$MPG^2$]')\n",
    "  plt.plot(hist['epoch'], hist['mse'],\n",
    "           label='Train Error')\n",
    "  plt.plot(hist['epoch'], hist['val_mse'],\n",
    "           label = 'Val Error')\n",
    "  plt.ylim([0,20])\n",
    "  plt.legend()\n",
    "  plt.show()\n",
    "\n",
    "\n",
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AqsuANc11FYv"
   },
   "source": [
    "该图表显示在约100个 epochs 之后误差非但没有改进，反而出现恶化。 让我们更新 `model.fit` 调用，当验证值没有提高上是自动停止训练。\n",
    "我们将使用一个 *EarlyStopping callback* 来测试每个 epoch 的训练条件。如果经过一定数量的 epochs 后没有改进，则自动停止训练。\n",
    "\n",
    "你可以从[这里](https://tensorflow.google.cn/versions/master/api_docs/python/tf/keras/callbacks/EarlyStopping)学习到更多的回调。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-22T22:15:02.606676Z",
     "iopub.status.busy": "2020-09-22T22:15:02.605975Z",
     "iopub.status.idle": "2020-09-22T22:15:07.787651Z",
     "shell.execute_reply": "2020-09-22T22:15:07.787037Z"
    },
    "id": "fdMZuhUgzMZ4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......."
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...\n",
      "..."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...."
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    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = build_model()\n",
    "\n",
    "# patience 值用来检查改进 epochs 的数量\n",
    "early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)\n",
    "\n",
    "history = model.fit(normed_train_data, train_labels, epochs=EPOCHS,\n",
    "                    validation_split = 0.2, verbose=0, callbacks=[early_stop, PrintDot()])\n",
    "\n",
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3St8-DmrX8P4"
   },
   "source": [
    "如图所示，验证集中的平均的误差通常在 +/- 2 MPG左右。 这个结果好么？ 我们将决定权留给你。\n",
    "\n",
    "让我们看看通过使用 **测试集** 来泛化模型的效果如何，我们在训练模型时没有使用测试集。这告诉我们，当我们在现实世界中使用这个模型时，我们可以期望它预测得有多好。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-22T22:15:07.792511Z",
     "iopub.status.busy": "2020-09-22T22:15:07.791872Z",
     "iopub.status.idle": "2020-09-22T22:15:07.848202Z",
     "shell.execute_reply": "2020-09-22T22:15:07.847653Z"
    },
    "id": "jl_yNr5n1kms"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 - 0s - loss: 5.9941 - mae: 1.8809 - mse: 5.9941\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing set Mean Abs Error:  1.88 MPG\n"
     ]
    }
   ],
   "source": [
    "loss, mae, mse = model.evaluate(normed_test_data, test_labels, verbose=2)\n",
    "\n",
    "print(\"Testing set Mean Abs Error: {:5.2f} MPG\".format(mae))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ft603OzXuEZC"
   },
   "source": [
    "### 做预测\n",
    " \n",
    "最后，使用测试集中的数据预测 MPG 值:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-22T22:15:07.854195Z",
     "iopub.status.busy": "2020-09-22T22:15:07.853510Z",
     "iopub.status.idle": "2020-09-22T22:15:08.044984Z",
     "shell.execute_reply": "2020-09-22T22:15:08.045433Z"
    },
    "id": "Xe7RXH3N3CWU"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_predictions = model.predict(normed_test_data).flatten()\n",
    "\n",
    "plt.scatter(test_labels, test_predictions)\n",
    "plt.xlabel('True Values [MPG]')\n",
    "plt.ylabel('Predictions [MPG]')\n",
    "plt.axis('equal')\n",
    "plt.axis('square')\n",
    "plt.xlim([0,plt.xlim()[1]])\n",
    "plt.ylim([0,plt.ylim()[1]])\n",
    "_ = plt.plot([-100, 100], [-100, 100])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "19wyogbOSU5t"
   },
   "source": [
    "这看起来我们的模型预测得相当好。我们来看下误差分布。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-22T22:15:08.060976Z",
     "iopub.status.busy": "2020-09-22T22:15:08.060086Z",
     "iopub.status.idle": "2020-09-22T22:15:08.212229Z",
     "shell.execute_reply": "2020-09-22T22:15:08.212673Z"
    },
    "id": "f-OHX4DiXd8x"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "error = test_predictions - test_labels\n",
    "plt.hist(error, bins = 25)\n",
    "plt.xlabel(\"Prediction Error [MPG]\")\n",
    "_ = plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m0CB5tBjSU5w"
   },
   "source": [
    "它不是完全的高斯分布，但我们可以推断出，这是因为样本的数量很小所导致的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vgGQuV-yqYZH"
   },
   "source": [
    "## 结论\n",
    "\n",
    "本笔记本 (notebook) 介绍了一些处理回归问题的技术。\n",
    "\n",
    "* 均方误差（MSE）是用于回归问题的常见损失函数（分类问题中使用不同的损失函数）。\n",
    "* 类似的，用于回归的评估指标与分类不同。 常见的回归指标是平均绝对误差（MAE）。\n",
    "* 当数字输入数据特征的值存在不同范围时，每个特征应独立缩放到相同范围。\n",
    "* 如果训练数据不多，一种方法是选择隐藏层较少的小网络，以避免过度拟合。\n",
    "* 早期停止是一种防止过度拟合的有效技术。"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "regression.ipynb",
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
